Speech offers important benefits for mental health monitoring, especially since certain conditions such as depression are common mental health problems that have significant impact on human society but often go undetected. Speech analysis is noninvasive, natural, inexpensive, and can be used in a number of emerging areas such as telemedicine applications for remote assessments, for example.
There is evidence in the psycholinguistics literature that speech contains important information that may assist psychiatrists during clinical assessment. Central controls of laryngeal, pharyngeal, and nasal structures tend to generate several objectively measurable expressions of emotional stress. Mental health problems including schizophrenia, depression, and psychopathy, for example, generally affect prosody. Indeed, psychological health appears to be intimately linked to producing certain types of speech characteristics.
An important challenge is individual differences, which are typically large and can obscure inter-speaker effects associated with mental health. As part of a larger effort on longitudinal speaker-state modeling, speech characteristics that correlate with clinical assessments about a patients suicide risk may be identified.
A new corpus of real patient-clinician interactions recorded when patients are admitted to a hospital for suicide risk may be analyzed, and again when the patients are released. For privacy reasons, only non-lexical features are typically used. Specific changes to automatically extracted speech features that correlate with a clinician's assessment for a patient's depression state may be identified.
Indeed, various prior studies have attempted to identify speech characteristics that can detect different psychological conditions.